| 1 | Protein kinase C/ζ (PRKCZ) Gene is associated with type 2 diabetes in Han population of North China and analysis of its haplotypes显示文摘AIM: To identify the susceptible gene (s) for type 2 diabetes in the prevousely mapped region, 1p36.33-p36.23, in Han population of North China using single nucleotide polymorphisms (SNPs) and to analyze the haplotypes of the gene (s) related to type 2 diabetes.METHODS: Twenty three SNPs located in 10 candidate genes in the mapped region were chosen from public SNP domains with bioinformatic methods, and the single base extension (SBE) method was used to genotype the loci for 192 sporadic type 2 diabetes patients and 172 normal individuals, all with Hah ethical origin, to perform this casecontrol study. The haplotypes with significant difference in the gene (s) were further analyzed.RFSULTS: Among the 23 SNPs, 8 were found to be common in Chinese Han population. Allele frequency of one SNP,rs436045 in the protein kinase C/ζgene (PRKCZ) was statistically different between the case and control groups (P<0.05). Furthermore, haplotypes at five SNP sites of PRKCZ gene were identified.CONCLUSION: PRKCZgene may be associated with type 2 diabetes in Hah population in North China. The haplotypes at five SNP sites in this gene may be responsible for this association. | Yun-FengLi Hong-XiaSun Guo-DonqWu Wei-NanDu JinZuo YanShen Bo-QinQiang Zhi-JianYao HengWang WeiHnang ZhuChen Mo-MiaoXiong YanMeng Fu-DeFang | 2003 | World Journal of Gastroenterology2003，9，9： | 5 |
| 2 | Cy-CNN:cylinder convolution based rotation-invariant neural network for point cloud registration显示文摘Point cloud registration is a challenging problem in the condition of large initial misalignments and noises.A major problem encountered in the registration algorithms is the definition of correspondence between two point clouds.Point clouds contain rich geometric information and the same geometric structure implies the same feature even if they are in diferent poses,which motivates us to seek a rotation-invariant feature representation for calculating the correspondence.This work proposes a rotation-invariant neural network for point cloud registration.To acquire rotation-invariant features,we firstly propose a rotationinvariant point cloud representation(RIPR)at the input level.Instead of using the original coordinates,we propose to use point pair features(PPF)and the transformed coordinates in the local reference frame(LRF)to represent a point.Then,we design a new convolution operator named Cylinder-Conv which utilizes the symmetry of cylinder-shaped voxels and the hierarchical geometry information of the surface of3D shapes.By specifying the cylinder-shaped structures and directions,Cylinder-Conv can better capture the local neighborhood geometry of each point and maintain rotation-invariance.Finally,we combine RIPR and Cylinder-Conv to extract normalized rotation-invariant features to generate the correspondence and perform a diferentiable singular value decomposition(SVD)step to estimate the rigid transformation.The proposed network presents state-of-the-art performance on point cloud registration.Experiments show that our method is robust to initial misalignments and noises. | Hengwang ZHAO Zhidong LIANG Yuesheng HE Chunxiang WANG Ming YANG | 2023 | Science China(Information Sciences)2023，66，5： | 0 |
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